The benefits and risks of bacille Calmette-Guérin vaccination among infants at high risk for both tuberculosis and severe combined immunodeficiency: assessment by Markov model
Bibliographic record
Abstract
BACKGROUND: Bacille Calmette-Guérin (BCG) vaccine is given to Canadian Aboriginal neonates in selected communities. Severe reactions and deaths associated with BCG have been reported among infants born with immunodeficiency syndromes. The main objective of this study was to estimate threshold values for severe combined immunodeficiency (SCID) incidence, above which BCG is associated with greater risk than benefit. METHODS: A Markov model was developed to simulate the natural histories of tuberculosis (TB) and SCID in children from birth to 14 years. The annual risk of tuberculous infection (ARI) and SCID incidence were varied in analyses. The model compared a scenario of no vaccination to intervention with BCG. Appropriate variability and uncertainty analyses were conducted. Outcomes included TB incidence and quality-adjusted life years (QALYs). RESULTS: In sensitivity analyses, QALYs were lower among vaccinated infants if the ARI was 0.1% and the rate of SCID was higher than 4.2 per 100,000. Assuming an ARI of 1%, this threshold increased to 41 per 100,000. In uncertainty analyses (Monte Carlo simulations) which assumed an ARI of 0.1%, QALYs were not significantly increased by BCG unless SCID incidence is 0. With this ARI, QALYs were significantly decreased among vaccinated children if SCID incidence exceeds 23 per 100,000. BCG is associated with a significant increase in QALYs if the ARI is 1%, and SCID incidence is below 5 per 100,000. CONCLUSION: The possibility that Canadian Aboriginal children are at increased risk for SCID has serious implications for continued BCG use in this population. In this context, enhanced TB Control--including early detection and treatment of infection--may be a safer, more effective alternative.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".